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Following the rapid development of various industrial sectors, air pollution frequently occurs in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 can cause highly detrimental effects on human health. This study aims to predict the daily average concentration of PM10 based on the data collected from 60 air quality monitoring stations in Malaysia. Building a forecasting model for each station is time-consuming and unrealistic; therefore, a hybrid model that combines the k-means clustering technique and the long short-term memory (LSTM) model is proposed to reduce the number of models and the overall model training time. Based on the training set, the stations were clustered using the k-means algorithm and an LSTM model was built for each cluster. Then, the prediction performance of the hybrid model was compared with the univariate LSTM model built independently for each station. The results show that the hybrid model has a comparable prediction performance to the univariate LSTM model, as it gives the relative percentage difference (RPD) less than or equal to 50% based on at least two accuracy metrics for 43 stations. The hybrid model can also fit the actual data trend well with a much shorter training time. Hence, the hybrid model is more competitive and suitable for real applications to forecast air quality. [ABSTRACT FROM AUTHOR]

Background: COPD is a heterogeneous disease demonstrating inter-individual variation. A high COPD prevalence in Chinese populations is described, but little is known about disease clusters and prognostic outcomes in the Chinese population across Southeast Asia. We aim to determine if clusters of Chinese patients with COPD exist and their association with systemic inflammation and clinical outcomes.Research Question: We aim to determine if clusters of Chinese patients with COPD exist and their association with clinical outcomes and inflammation.Study Design and Methods: Chinese patients with stable COPD were prospectively recruited into two cohorts (derivation and validation) from six hospitals across three Southeast Asian countries (Singapore, Malaysia, and Hong Kong; n = 1,480). Each patient was followed more than 2 years. Clinical data (including co-morbidities) were employed in unsupervised hierarchical clustering (followed by validation) to determine the existence of patient clusters and their prognostic outcome. Accompanying systemic cytokine assessments were performed in a subset (n = 336) of patients with COPD to determine if inflammatory patterns and associated networks characterized the derived clusters.Results: Five patient clusters were identified including: (1) ex-TB, (2) diabetic, (3) low comorbidity: low-risk, (4) low comorbidity: high-risk, and (5) cardiovascular. The cardiovascular and ex-TB clusters demonstrate highest mortality (independent of Global Initiative for Chronic Obstructive Lung Disease assessment) and illustrate diverse cytokine patterns with complex inflammatory networks.Interpretation: We describe clusters of Chinese patients with COPD, two of which represent high-risk clusters. The cardiovascular and ex-TB patient clusters exhibit high mortality, significant inflammation, and complex cytokine networks. Clinical and inflammatory risk stratification of Chinese patients with COPD should be considered for targeted intervention to improve disease outcomes. [ABSTRACT FROM AUTHOR]